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Found 3 papers, 1 papers with code
Taming Hyperparameter Tuning in Continuous Normalizing Flows Using the JKO Scheme
Instead of tuning $\alpha$, we repeatedly solve the optimization problem for a fixed $\alpha$ effectively performing a JKO update with a time-step $\alpha$.
Estimating a potential without the agony of the partition function
In this paper we propose an alternative approach based on Maximum A-Posteriori (MAP) estimators, we name Maximum Recovery MAP (MR-MAP), to derive estimators that do not require the computation of the partition function, and reformulate the problem as an optimization problem.
Stochastic Newton and Quasi-Newton Methods for Large Linear Least-squares Problems
We describe stochastic Newton and stochastic quasi-Newton approaches to efficiently solve large linear least-squares problems where the very large data sets present a significant computational burden (e. g., the size may exceed computer memory or data are collected in real-time).
